Cody

# Problem 44507. Curve fitting (linear functions) & function handles

Solution 1738227

Submitted on 27 Feb 2019 by Martin C.
This solution is locked. To view this solution, you need to provide a solution of the same size or smaller.

### Test Suite

Test Status Code Input and Output
1   Pass
x = 1:100; y_correct = 2:2:200; [fh, pars] = generateFit(x, y_correct); y = fh(pars, x); assert( isequal(y,y_correct) )

asetofparameters = 2.0000 -0.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 Columns 31 through 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118 120 Columns 61 through 90 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178 180 Columns 91 through 100 182 184 186 188 190 192 194 196 198 200

2   Pass
x = 1:100; y_correct = 101:200; [fh, pars] = generateFit(x, y_correct); y = fh(pars, x); assert( isequal(y,y_correct) )

asetofparameters = 1.0000 100.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 Columns 31 through 60 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 Columns 61 through 90 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 Columns 91 through 100 191 192 193 194 195 196 197 198 199 200

3   Pass
x = 1:100; y_correct = 102:2:300; [fh, pars] = generateFit(x, y_correct); y = fh(pars, x); assert( isequal(y,y_correct) )

asetofparameters = 2.0000 100.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 Columns 31 through 60 162 164 166 168 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214 216 218 220 Columns 61 through 90 222 224 226 228 230 232 234 236 238 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268 270 272 274 276 278 280 Columns 91 through 100 282 284 286 288 290 292 294 296 298 300

4   Pass
x = 1:100; y_correct = 102:2:300; [fh, pars] = generateFit(x, y_correct); els = 10+randi(30) : 60+randi(30); y = fh(pars, x(els)); assert( isequal(y,y_correct(els)) ) y = fh(pars, x+100); assert( isequal(y,302:2:500) )

asetofparameters = 2.0000 100.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178 180 182 184 186 Columns 31 through 49 188 190 192 194 196 198 200 202 204 206 208 210 212 214 216 218 220 222 224 y = Columns 1 through 30 302 304 306 308 310 312 314 316 318 320 322 324 326 328 330 332 334 336 338 340 342 344 346 348 350 352 354 356 358 360 Columns 31 through 60 362 364 366 368 370 372 374 376 378 380 382 384 386 388 390 392 394 396 398 400 402 404 406 408 410 412 414 416 418 420 Columns 61 through 90 422 424 426 428 430 432 434 436 438 440 442 444 446 448 450 452 454 456 458 460 462 464 466 468 470 472 474 476 478 480 Columns 91 through 100 482 484 486 488 490 492 494 496 498 500

5   Pass
x = 1000:-1:500; y_correct = 0:500; [fh, pars] = generateFit(x, y_correct); y = fh(pars, x); assert( isequal(y,y_correct) )

asetofparameters = 1.0e+03 * -0.0010 1.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Columns 31 through 60 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Columns 61 through 90 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 Columns 91 through 120 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 Columns 121 through 150 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 Columns 151 through 180 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 Columns 181 through 210 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 Columns 211 through 240 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 Columns 241 through 270 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 Columns 271 through 300 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 Columns 301 through 330 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 Columns 331 through 360 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 Columns 361 through 390 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 Columns 391 through 420 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 Columns 421 through 450 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 Columns 451 through 480 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 Columns 481 through 501 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500

6   Pass
x = 1000:-1:500; y_correct = -500:2:500; [fh, pars] = generateFit(x, y_correct); y = fh(pars, x); assert( isequal(y,y_correct) )

asetofparameters = 1.0e+03 * -0.0020 1.5000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 -500 -498 -496 -494 -492 -490 -488 -486 -484 -482 -480 -478 -476 -474 -472 -470 -468 -466 -464 -462 -460 -458 -456 -454 -452 -450 -448 -446 -444 -442 Columns 31 through 60 -440 -438 -436 -434 -432 -430 -428 -426 -424 -422 -420 -418 -416 -414 -412 -410 -408 -406 -404 -402 -400 -398 -396 -394 -392 -390 -388 -386 -384 -382 Columns 61 through 90 -380 -378 -376 -374 -372 -370 -368 -366 -364 -362 -360 -358 -356 -354 -352 -350 -348 -346 -344 -342 -340 -338 -336 -334 -332 -330 -328 -326 -324 -322 Columns 91 through 120 -320 -318 -316 -314 -312 -310 -308 -306 -304 -302 -300 -298 -296 -294 -292 -290 -288 -286 -284 -282 -280 -278 -276 -274 -272 -270 -268 -266 -264 -262 Columns 121 through 150 -260 -258 -256 -254 -252 -250 -248 -246 -244 -242 -240 -238 -236 -234 -232 -230 -228 -226 -224 -222 -220 -218 -216 -214 -212 -210 -208 -206 -204 -202 Columns 151 through 180 -200 -198 -196 -194 -192 -190 -188 -186 -184 -182 -180 -178 -176 -174 -172 -170 -168 -166 -164 -162 -160 -158 -156 -154 -152 -150 -148 -146 -144 -142 Columns 181 through 210 -140 -138 -136 -134 -132 -130 -128 -126 -124 -122 -120 -118 -116 -114 -112 -110 -108 -106 -104 -102 -100 -98 -96 -94 -92 -90 -88 -86 -84 -82 Columns 211 through 240 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 Columns 241 through 270 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 Columns 271 through 300 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Columns 301 through 330 100 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 Columns 331 through 360 160 162 164 166 168 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214 216 218 Columns 361 through 390 220 222 224 226 228 230 232 234 236 238 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268 270 272 274 276 278 Columns 391 through 420 280 282 284 286 288 290 292 294 296 298 300 302 304 306 308 310 312 314 316 318 320 322 324 326 328 330 332 334 336 338 Columns 421 through 450 340 342 344 346 348 350 352 354 356 358 360 362 364 366 368 370 372 374 376 378 380 382 384 386 388 390 392 394 396 398 Columns 451 through 480 400 402 404 406 408 410 412 414 416 418 420 422 424 426 428 430 432 434 436 438 440 442 444 446 448 450 452 454 456 458 Columns 481 through 501 460 462 464 466 468 470 472 474 476 478 480 482 484 486 488 490 492 494 496 498 500

7   Pass
for i = 1 : 20 x = -randi(1000) : randi(10) : randi(1000); m = randi(20)-10; c = randi(20)-10; y_correct = round(exp(log(c) + log(x))) - m^1 * (x(1))^0; [fh, pars] = generateFit(x, y_correct); y = fh(pars, x); assert( isequal(y,y_correct) ) end;

asetofparameters = 3.0000 -2.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 -260 -248 -236 -224 -212 -200 -188 -176 -164 -152 -140 -128 -116 -104 -92 -80 -68 -56 -44 -32 -20 -8 4 16 28 40 52 64 76 88 Columns 31 through 60 100 112 124 136 148 160 172 184 196 208 220 232 244 256 268 280 292 304 316 328 340 352 364 376 388 400 412 424 436 448 Columns 61 through 89 460 472 484 496 508 520 532 544 556 568 580 592 604 616 628 640 652 664 676 688 700 712 724 736 748 760 772 784 796 asetofparameters = 9.0000 -0.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 15 -2250 -2187 -2124 -2061 -1998 -1935 -1872 -1809 -1746 -1683 -1620 -1557 -1494 -1431 -1368 Columns 16 through 30 -1305 -1242 -1179 -1116 -1053 -990 -927 -864 -801 -738 -675 -612 -549 -486 -423 Columns 31 through 45 -360 -297 -234 -171 -108 -45 18 81 144 207 270 333 396 459 522 Columns 46 through 60 585 648 711 774 837 900 963 1026 1089 1152 1215 1278 1341 1404 1467 Columns 61 through 75 1530 1593 1656 1719 1782 1845 1908 1971 2034 2097 2160 2223 2286 2349 2412 Columns 76 through 90 2475 2538 2601 2664 2727 2790 2853 2916 2979 3042 3105 3168 3231 3294 3357 Columns 91 through 105 3420 3483 3546 3609 3672 3735 3798 3861 3924 3987 4050 4113 4176 4239 4302 Columns 106 through 120 4365 4428 4491 4554 4617 4680 4743 4806 4869 4932 4995 5058 5121 5184 5247 Columns 121 through 135 5310 5373 5436 5499 5562 5625 5688 5751 5814 5877 5940 6003 6066 6129 6192 Columns 136 through 150 6255 6318 6381 6444 6507 6570 6633 6696 6759 6822 6885 6948 7011 7074 7137 Columns 151 through 165 7200 7263 7326 7389 7452 7515 7578 7641 7704 7767 7830 7893 7956 8019 8082 Columns 166 through 172 8145 8208 8271 8334 8397 8460 8523 asetofparameters = -7.0000 1.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 15 3984 3935 3886 3837 3788 3739 3690 3641 3592 3543 3494 3445 3396 3347 3298 Columns 16 through 30 3249 3200 3151 3102 3053 3004 2955 2906 2857 2808 2759 2710 2661 2612 2563 Columns 31 through 45 2514 2465 2416 2367 2318 2269 2220 2171 2122 2073 2024 1975 1926 1877 1828 Columns 46 through 60 1779 1730 1681 1632 1583 1534 1485 1436 1387 1338 1289 1240 1191 1142 1093 Columns 61 through 75 1044 995 946 897 848 799 750 701 652 603 554 505 456 407 358 Columns 76 through 90 309 260 211 162 113 64 15 -34 -83 -132 -181 -230 -279 -328 -377 Columns 91 through 105 -426 -475 -524 -573 -622 -671 -720 -769 -818 -867 -916 -965 -1014 -1063 -1112 Columns 106 through 120 -1161 -1210 -1259 -1308 -1357 -1406 -1455 -1504 -1553 -1602 -1651 -1700 -1749 -1798 -1847 Columns 121 through 135 -1896 -1945 -1994 -2043 -2092 -2141 -2190 -2239 -2288 -2337 -2386 -2435 -2484 -2533 -2582 Column 136 -2631 asetofparameters = -7.0000 8.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 15 4586 4572 4558 4544 4530 4516 4502 4488 4474 4460 4446 4432 4418 4404 4390 Columns 16 through 30 4376 4362 4348 4334 4320 4306 4292 4278 4264 4250 4236 4222 4208 4194 4180 Columns 31 through 45 4166 4152 4138 4124 4110 4096 4082 4068 4054 4040 4026 4012 3998 3984 3970 Columns 46 through 60 3956 3942 3928 3914 3900 3886 3872 3858 3844 3830 3816 3802 3788 3774 3760 Columns 61 through 75 3746 3732 3718 3704 3690 3676 3662 3648 3634 3620 3606 3592 3578 3564 3550 Columns 76 through 90 3536 3522 3508 3494 3480 3466 3452 3438 3424 3410 3396 3382 3368 3354 3340 Columns 91 through 105 3326 3312 3298 3284 3270 3256 3242 3228 3214 3200 3186 3172 3158 3144 3130 Columns 106 through 120 3116 3102 3088 3074 3060 3046 3032 3018 3004 2990 2976 2962 2948 2934 2920 Columns 121 through 135 2906 2892 2878 2864 2850 2836 2822 2808 2794 2780 2766 2752 2738 2724 2710 Columns 136 through 150 2696 2682 2668 2654 2640 2626 2612 2598 2584 2570 2556 2542 2528 2514 2500 Columns 151 through 165 2486 2472 2458 2444 2430 2416 2402 2388 2374 2360 2346 2332 2318 2304 2290 Columns 166 through 180 2276 2262 2248 2234 2220 2206 2192 2178 2164 2150 2136 2122 2108 2094 2080 Columns 181 through 195 2066 2052 2038 2024 2010 1996 1982 1968 1954 1940 1926 1912 1898 1884 1870 Columns 196 through 210 1856 1842 1828 1814 1800 1786 1772 1758 1744 1730 1716 1702 1688 1674 1660 Columns 211 through 225 1646 1632 1618 1604 1590 1576 1562 1548 1534 1520 1506 1492 1478 1464 1450 Columns 226 through 240 1436 1422 1408 1394 1380 1366 1352 1338 1324 1310 1296 1282 1268 1254 1240 Columns 241 through 255 1226 1212 1198 1184 1170 1156 1142 1128 1114 1100 1086 1072 1058 1044 1030 Columns 256 through 270 1016 1002 988 974 960 946 932 918 904 890 876 862 848 834 820 Columns 271 through 285 806 792 778 764 750 736 722 708 694 680 666 652 638 624 610 Columns 286 through 300 596 582 568 554 540 526 512 498 484 470 456 442 428 414 400 Columns 301 through 315 386 372 358 344 330 316 302 288 274 260 246 232 218 204 190 Columns 316 through 330 176 162 148 134 120 106 92 78 64 50 36 ...

8   Pass
% "Gradient and intercept" x1 = 1:100; y1_correct = 102:2:300; [fh1, pars1] = generateFit(x1, y1_correct); % "Another gradient and intercept" x2 = 1000:-1:500; y2_correct = 0:500; [fh2, pars2] = generateFit(x2, y2_correct); % According to the Problem Statament, fh1 and fh2 should be interchangeable. y1 = fh2(pars1, x1); assert( isequal(y1,y1_correct) ) y2 = fh1(pars2, x2); assert( isequal(y2,y2_correct) )

asetofparameters = 2.0000 100.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction asetofparameters = 1.0e+03 * -0.0010 1.0000 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction y = Columns 1 through 30 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 Columns 31 through 60 162 164 166 168 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214 216 218 220 Columns 61 through 90 222 224 226 228 230 232 234 236 238 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268 270 272 274 276 278 280 Columns 91 through 100 282 284 286 288 290 292 294 296 298 300 y = Columns 1 through 30 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Columns 31 through 60 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Columns 61 through 90 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 Columns 91 through 120 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 Columns 121 through 150 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 Columns 151 through 180 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 Columns 181 through 210 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 Columns 211 through 240 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 Columns 241 through 270 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 Columns 271 through 300 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 Columns 301 through 330 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 Columns 331 through 360 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 Columns 361 through 390 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 Columns 391 through 420 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 Columns 421 through 450 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 Columns 451 through 480 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 Columns 481 through 501 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500

9   Pass
% Finally, check that the user is sending a small number of parameters to their % custom function (to be called via the function handle), and not simply sending % the entire vector y. x = 1:1000; y_correct = flip(1:1000); [fh, pars] = generateFit(x, y_correct); pw = whos('pars') assert( pw.bytes < 100 , 'Parameter variable is too big.') y = fh(pars, x); assert( isequal(y,y_correct) )

asetofparameters = 1.0e+03 * -0.0010 1.0010 afunctionhandle = function_handle with value: @nameofyourgenericlinearfunction pw = struct with fields: name: 'pars' size: [1 2] bytes: 16 class: 'double' global: 0 sparse: 0 complex: 0 nesting: [1×1 struct] persistent: 0 y = Columns 1 through 15 1000 999 998 997 996 995 994 993 992 991 990 989 988 987 986 Columns 16 through 30 985 984 983 982 981 980 979 978 977 976 975 974 973 972 971 Columns 31 through 45 970 969 968 967 966 965 964 963 962 961 960 959 958 957 956 Columns 46 through 60 955 954 953 952 951 950 949 948 947 946 945 944 943 942 941 Columns 61 through 75 940 939 938 937 936 935 934 933 932 931 930 929 928 927 926 Columns 76 through 90 925 924 923 922 921 920 919 918 917 916 915 914 913 912 911 Columns 91 through 105 910 909 908 907 906 905 904 903 902 901 900 899 898 897 896 Columns 106 through 120 895 894 893 892 891 890 889 888 887 886 885 884 883 882 881 Columns 121 through 135 880 879 878 877 876 875 874 873 872 871 870 869 868 867 866 Columns 136 through 150 865 864 863 862 861 860 859 858 857 856 855 854 853 852 851 Columns 151 through 165 850 849 848 847 846 845 844 843 842 841 840 839 838 837 836 Columns 166 through 180 835 834 833 832 831 830 829 828 827 826 825 824 823 822 821 Columns 181 through 195 820 819 818 817 816 815 814 813 812 811 810 809 808 807 806 Columns 196 through 210 805 804 803 802 801 800 799 798 797 796 795 794 793 792 791 Columns 211 through 225 790 789 788 787 786 785 784 783 782 781 780 779 778 777 776 Columns 226 through 240 775 774 773 772 771 770 769 768 767 766 765 764 763 762 761 Columns 241 through 255 760 759 758 757 756 755 754 753 752 751 750 749 748 747 746 Columns 256 through 270 745 744 743 742 741 740 739 738 737 736 735 734 733 732 731 Columns 271 through 285 730 729 728 727 726 725 724 723 722 721 720 719 718 717 716 Columns 286 through 300 715 714 713 712 711 710 709 708 707 706 705 704 703 702 701 Columns 301 through 315 700 699 698 697 696 695 694 693 692 691 690 689 688 687 686 Columns 316 through 330 685 684 683 682 681 680 679 678 677 676 675 674 673 672 671 Columns 331 through 345 670 669 668 667 666 665 664 663 662 661 660 659 658 657 656 Columns 346 through 360 655 654 653 652 651 650 649 648 647 646 645 644 643 642 641 Columns 361 through 375 640 639 638 637 636 635 634 633 632 631 630 629 628 627 626 Columns 376 through 390 625 624 623 622 621 620 619 618 617 616 615 614 613 612 611 Columns 391 through 405 610 609 608 607 606 605 604 603 602 601 600 599 598 597 596 Columns 406 through 420 595 594 593 592 591 590 589 588 587 586 585 584 583 582 581 Columns 421 through 435 580 579 578 577 576 575 574 573 572 571 570 569 568 567 566 Columns 436 through 450 565 564 563 562 561 560 559 558 557 556 555 554 553 552 551 Columns 451 through 465 550 549 548 547 546 545 544 543 542 541 540 539 538 537 536 Columns 466 through 480 535 534 533 532 531 530 529 528 527 526 525 524 523 522 521 Columns 481 through 495 520 519 518 517 516 515 514 513 512 511 510 509 508 507 506 Columns 496 through 510 505 504 503 502 501 500 499 498 497 496 495 494 493 492 491 Columns 511 through 525 490 489 488 487 486 485 484 483 482 481 480 479 478 477 476 Columns 526 through 540 475 474 473 472 471 470 469 468 467 466 465 464 463 462 461 Columns 541 through 555 460 459 458 457 456 455 454 453 452 451 450 449 448 447 446 Columns 556 through 570 445 444 443 442 441 440 439 438 437 436 435 434 433 432 431 Columns 571 through 585 430 429 428 427 426 425 424 423 422 421 420 419 418 417 416 Columns 586 through 600 415 414 413 412 411 410 409 408 407 406 405 404 403 402 401 Columns 601 through 615 400 399 398 397 396 395 394 393 392 391 390 389 388 387 386 Columns 616 through 630 385 384 383 382 381 380 379 378 377 376 375 374 373 372 371 Columns 631 through 645 370 369 368 367 366 365 364 363 362 361 360 359 358 357 356 Columns 646 through 660 355 354 353 352 351 350 349 348 347 346 345 344 343 342 341 Columns 661 through 675 340 339 338 337 336 335 334 333 332 331 330 329 328 327 326 Columns 676 through 690 325 324 323 322 321 320 319 318 317 316 315 314 313 312 311 Columns 691 through 705 ...

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